Rough Set Theory: A True Landmark in Data Analysis

✍ Scribed by B. K. Tripathy (auth.), Ajith Abraham, Rafael Falcón, Rafael Bello (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2009
• Tongue: English
• Leaves: 327
• Series: Studies in Computational Intelligence 174
• Edition: 1
• Category: Library

✦ Synopsis

Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.

✦ Table of Contents

Front Matter....Pages -
Front Matter....Pages 1-1
Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces....Pages 3-44
Categorical Innovations for Rough Sets....Pages 45-69
Granular Structures and Approximations in Rough Sets and Knowledge Spaces....Pages 71-84
On Approximation of Classifications, Rough Equalities and Rough Equivalences....Pages 85-133
Front Matter....Pages 135-135
Rough Clustering with Partial Supervision....Pages 137-161
A Generic Scheme for Generating Prediction Rules Using Rough Sets....Pages 163-186
Rough Web Caching....Pages 187-211
Software Defect Classification: A Comparative Study of Rough-Neuro-fuzzy Hybrid Approaches with Linear and Non-linear SVMs....Pages 213-231
Front Matter....Pages 233-233
Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem....Pages 235-260
Nature Inspired Population-Based Heuristics for Rough Set Reduction....Pages 261-278
Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis....Pages 279-320
Back Matter....Pages -

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)